Add SetFit model

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+unigram.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: ' oui le problème est plus psychologique, je suis d''accord avec vous, et
+    je pense que si l''état mettait en place des suivis psychologiques vraiment poussés,
+    il n''y aurait pas d''opération, moi j''ai eu un bypass, j''ai repris une quinzaine
+    de kilos bien malheureusement, et l''aide pour l''addiction n''est pas prise en
+    charge, genre thérapie, et moi je sais que c''est cela mon problème, quand on
+    n''a pas une bonne image de soi même, c''est très difficile de gagner la partie'
+- text: Ouaw je me suis totalement reconnue, c’est exactement ce que je vis après
+    être sortie d’une sorte d’orthorexie qui a duré plus d’un an et demi (limite anorexie
+    mentale), pleine de frustrations j’ai donc repris une dizaine de kilos et ma vie
+    est rythmée de privation et d’hyperphagie.. j’essaye de ne pas trop me priver
+    mais j’ai l’impression d’être constamment frustrée.. merci pour ta vidéo ❤️
+- text: Mdr mais faut pas être médecin pour savoir comment fonctionne le corps humain
+    tu donne meme pas d'argument. Tu dis que c'est une maladie bah parle en dis moi
+    pourquoi comment ??? Parce que la j'entends que des tu dis de la merde t médecin
+    nanani nanana. Toi en tout cas tu dois pas être Einstein. Moi j'ai donné tout
+    les argument qui faut en plus ils sont prouvé. Tu mange tu grossit tu mange pas
+    tu minci. Tu fais pas de sport tu maigrit pas tu fais du sport tu maigris bref
+    sa sert a rien de parler si ta meme pas la repartie de donner des argument à ce
+    que tu dis.
+- text: pour moi ce qui a fonctioner pour me libere de cette obsession, c'est mon
+    esprit critique. j'ai bien observee comment on se fout de moi pour que je consome
+    toujours plus de poison. apres j'ai bien vu comment on se fout de la soufrance
+    des animaux. et comment on nous eloigne de la nature et du naturel et saludable.
+    c'est un tres long processus d'observation sincere qui m'a aider a me liberer
+    peut a peut. aujourd'huit je suis vegan, je pratique la relaxation et la respiration
+    avec ou sans hypnose. il y a encore des moments ou je suis hanter par l'idee de
+    la bouffe, je ralentie, je respire lentement et profondement, je regarde le ciel,
+    les arbres, les chats, et je me demande comment l'etre humain arrive a etre tellement
+    miserable...
+- text: Comme je te comprend 🙏 mes tca ont commencé alors que j'avais 11 ans. J'espère
+    que tu vas mieux maintenant
+pipeline_tag: text-classification
+inference: false
+base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.6115702479338843
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 128 tokens
+<!-- - **Number of Classes:** Unknown -->
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6116   |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("Ghofranem/setfit-paraphrase-multilingual-MiniLM-L12-v2-ed-balanced-fr-AI4ED")
+# Run inference
+preds = model("Comme je te comprend 🙏 mes tca ont commencé alors que j'avais 11 ans. J'espère que tu vas mieux maintenant")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 4   | 95.7314 | 694 |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 10
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0017 | 1    | 0.2843        | -               |
+| 0.0826 | 50   | 0.2442        | -               |
+| 0.1653 | 100  | 0.2095        | -               |
+| 0.2479 | 150  | 0.193         | -               |
+| 0.3306 | 200  | 0.0896        | -               |
+| 0.4132 | 250  | 0.1439        | -               |
+| 0.4959 | 300  | 0.1234        | -               |
+| 0.5785 | 350  | 0.1074        | -               |
+| 0.6612 | 400  | 0.1135        | -               |
+| 0.7438 | 450  | 0.0982        | -               |
+| 0.8264 | 500  | 0.0392        | -               |
+| 0.9091 | 550  | 0.02          | -               |
+| 0.9917 | 600  | 0.043         | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.5.1
+- Transformers: 4.38.2
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.18.0
+- Tokenizers: 0.15.2
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 250037
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

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+  "normalize_embeddings": false,
+  "labels": null
+}

model.safetensors

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modules.json

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+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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